AI-driven digital twin and delay-aware surrogate MPC framework for biogas production

Anaerobic digesters exhibit nonlinear dynamics, long input–output delays, irregular sampling, and operational constraints that complicate biogas prediction and control. This study develops a delay-aware digital-twin MPC benchmarking framework in which Anaerobic Digestion Model No. 1 (ADM1) serves as a mechanistic reference plant, while established machine-learning surrogates (Random Forest, KNN, SVR, XGBoost, LSTM, and TabPFN) provide fast one-step predictions under irregular measurements. A unified workflow integrates time-stamp alignment, sliding-window reconstruction, and Bayesian hyperparameter optimization. The surrogates are evaluated on an industrial dataset and an ADM1-based simulator incorporating a 7-day actuator delay, seasonal variability, noise, and missing data. The trained models are embedded in a constrained MPC layer, where multi-day inputs are optimized using Bayesian Optimization or Particle Swarm Optimization under hard bounds and daily ramp-rate limits. Both open-loop replay and closed-loop digital-twin MPC are investigated. Results show that PSO–MPC with inexpensive surrogates achieves the largest methane gains (up to approximately 25%), whereas BO–MPC is preferable for computationally expensive surrogates due to superior sample efficiency. Closed-loop simulations demonstrate that steady-state performance is preserved through feedback correction despite surrogate mismatch. The primary contribution is a reproducible digital-twin MPC scaffold enabling systematic integration and benchmarking of surrogate–optimizer combinations. The framework provides a reusable evaluation testbed for data-driven control of slow, delay-dominated biochemical processes, with potential extension to other chemical and energy systems subject to long delays and irregular monitoring.